1,685 research outputs found
COMIC: Towards A Compact Image Captioning Model with Attention
Recent works in image captioning have shown very promising raw performance.
However, we realize that most of these encoder-decoder style networks with
attention do not scale naturally to large vocabulary size, making them
difficult to be deployed on embedded system with limited hardware resources.
This is because the size of word and output embedding matrices grow
proportionally with the size of vocabulary, adversely affecting the compactness
of these networks. To address this limitation, this paper introduces a brand
new idea in the domain of image captioning. That is, we tackle the problem of
compactness of image captioning models which is hitherto unexplored. We showed
that, our proposed model, named COMIC for COMpact Image Captioning, achieves
comparable results in five common evaluation metrics with state-of-the-art
approaches on both MS-COCO and InstaPIC-1.1M datasets despite having an
embedding vocabulary size that is 39x - 99x smaller. The source code and models
are available at:
https://github.com/jiahuei/COMIC-Compact-Image-Captioning-with-AttentionComment: Added source code link and new results in Table
Attentive Tensor Product Learning
This paper proposes a new architecture - Attentive Tensor Product Learning
(ATPL) - to represent grammatical structures in deep learning models. ATPL is a
new architecture to bridge this gap by exploiting Tensor Product
Representations (TPR), a structured neural-symbolic model developed in
cognitive science, aiming to integrate deep learning with explicit language
structures and rules. The key ideas of ATPL are: 1) unsupervised learning of
role-unbinding vectors of words via TPR-based deep neural network; 2) employing
attention modules to compute TPR; and 3) integration of TPR with typical deep
learning architectures including Long Short-Term Memory (LSTM) and Feedforward
Neural Network (FFNN). The novelty of our approach lies in its ability to
extract the grammatical structure of a sentence by using role-unbinding
vectors, which are obtained in an unsupervised manner. This ATPL approach is
applied to 1) image captioning, 2) part of speech (POS) tagging, and 3)
constituency parsing of a sentence. Experimental results demonstrate the
effectiveness of the proposed approach
Image Captioning and Classification of Dangerous Situations
Current robot platforms are being employed to collaborate with humans in a
wide range of domestic and industrial tasks. These environments require
autonomous systems that are able to classify and communicate anomalous
situations such as fires, injured persons, car accidents; or generally, any
potentially dangerous situation for humans. In this paper we introduce an
anomaly detection dataset for the purpose of robot applications as well as the
design and implementation of a deep learning architecture that classifies and
describes dangerous situations using only a single image as input. We report a
classification accuracy of 97 % and METEOR score of 16.2. We will make the
dataset publicly available after this paper is accepted
From Deterministic to Generative: Multi-Modal Stochastic RNNs for Video Captioning
Video captioning in essential is a complex natural process, which is affected
by various uncertainties stemming from video content, subjective judgment, etc.
In this paper we build on the recent progress in using encoder-decoder
framework for video captioning and address what we find to be a critical
deficiency of the existing methods, that most of the decoders propagate
deterministic hidden states. Such complex uncertainty cannot be modeled
efficiently by the deterministic models. In this paper, we propose a generative
approach, referred to as multi-modal stochastic RNNs networks (MS-RNN), which
models the uncertainty observed in the data using latent stochastic variables.
Therefore, MS-RNN can improve the performance of video captioning, and generate
multiple sentences to describe a video considering different random factors.
Specifically, a multi-modal LSTM (M-LSTM) is first proposed to interact with
both visual and textual features to capture a high-level representation. Then,
a backward stochastic LSTM (S-LSTM) is proposed to support uncertainty
propagation by introducing latent variables. Experimental results on the
challenging datasets MSVD and MSR-VTT show that our proposed MS-RNN approach
outperforms the state-of-the-art video captioning benchmarks
A Neural, Interactive-predictive System for Multimodal Sequence to Sequence Tasks
We present a demonstration of a neural interactive-predictive system for
tackling multimodal sequence to sequence tasks. The system generates text
predictions to different sequence to sequence tasks: machine translation, image
and video captioning. These predictions are revised by a human agent, who
introduces corrections in the form of characters. The system reacts to each
correction, providing alternative hypotheses, compelling with the feedback
provided by the user. The final objective is to reduce the human effort
required during this correction process.
This system is implemented following a client-server architecture. For
accessing the system, we developed a website, which communicates with the
neural model, hosted in a local server. From this website, the different tasks
can be tackled following the interactive-predictive framework. We open-source
all the code developed for building this system. The demonstration in hosted in
http://casmacat.prhlt.upv.es/interactive-seq2seq.Comment: ACL 2019 - System demonstration
STAIR Captions: Constructing a Large-Scale Japanese Image Caption Dataset
In recent years, automatic generation of image descriptions (captions), that
is, image captioning, has attracted a great deal of attention. In this paper,
we particularly consider generating Japanese captions for images. Since most
available caption datasets have been constructed for English language, there
are few datasets for Japanese. To tackle this problem, we construct a
large-scale Japanese image caption dataset based on images from MS-COCO, which
is called STAIR Captions. STAIR Captions consists of 820,310 Japanese captions
for 164,062 images. In the experiment, we show that a neural network trained
using STAIR Captions can generate more natural and better Japanese captions,
compared to those generated using English-Japanese machine translation after
generating English captions.Comment: Accepted as ACL2017 short paper. 5 page
Towards Accountable AI: Hybrid Human-Machine Analyses for Characterizing System Failure
As machine learning systems move from computer-science laboratories into the
open world, their accountability becomes a high priority problem.
Accountability requires deep understanding of system behavior and its failures.
Current evaluation methods such as single-score error metrics and confusion
matrices provide aggregate views of system performance that hide important
shortcomings. Understanding details about failures is important for identifying
pathways for refinement, communicating the reliability of systems in different
settings, and for specifying appropriate human oversight and engagement.
Characterization of failures and shortcomings is particularly complex for
systems composed of multiple machine learned components. For such systems,
existing evaluation methods have limited expressiveness in describing and
explaining the relationship among input content, the internal states of system
components, and final output quality. We present Pandora, a set of hybrid
human-machine methods and tools for describing and explaining system failures.
Pandora leverages both human and system-generated observations to summarize
conditions of system malfunction with respect to the input content and system
architecture. We share results of a case study with a machine learning pipeline
for image captioning that show how detailed performance views can be beneficial
for analysis and debugging
NMTPY: A Flexible Toolkit for Advanced Neural Machine Translation Systems
In this paper, we present nmtpy, a flexible Python toolkit based on Theano
for training Neural Machine Translation and other neural sequence-to-sequence
architectures. nmtpy decouples the specification of a network from the training
and inference utilities to simplify the addition of a new architecture and
reduce the amount of boilerplate code to be written. nmtpy has been used for
LIUM's top-ranked submissions to WMT Multimodal Machine Translation and News
Translation tasks in 2016 and 2017.Comment: 10 pages, 3 figure
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